#
# Copyright (C) 2023, Inria
# GRAPHDECO research group, https://team.inria.fr/graphdeco
# All rights reserved.
#
# This software is free for non-commercial, research and evaluation use 
# under the terms of the LICENSE.md file.
#
# For inquiries contact  george.drettakis@inria.fr
#

import os
import torch, torchvision
from random import randint
from utils.loss_utils import l1_loss, ssim, nerfw_loss
from gaussian_renderer import render, network_gui
import sys
from scene import Scene, GaussianModel
from utils.general_utils import safe_state
from tqdm import tqdm
from utils.image_utils import psnr
from argparse import ArgumentParser, Namespace
from arguments import ModelParams, PipelineParams, OptimizationParams
try:
    from torch.utils.tensorboard import SummaryWriter
    TENSORBOARD_FOUND = True
except ImportError:
    TENSORBOARD_FOUND = False
    

def training(dataset, opt, pipe, testing_iterations, saving_iterations, checkpoint_iterations, checkpoint, debug_from):
    first_iter = 0
    tb_writer = prepare_output_and_logger(dataset)
    
    # 创建 ‘GaussianModel’模型，给点云中的每个点创建一个 3D Gaussian
    gaussians = GaussianModel(dataset, opt.iterations)
    # 加载数据集和每张图片对应的 camera 的参数
    scene = Scene(dataset, gaussians)
    # 为 3D Gaussian 的各组参数创建 optimizer 和 lr_scheduler
    gaussians.training_setup(opt)
    # 加载模型参数
    if checkpoint:
        (model_params, first_iter) = torch.load(checkpoint)
        gaussians.restore(model_params, opt)

    # 设置背景颜色并放置 cuda 上
    bg_color = [1, 1, 1] if dataset.white_background else [0, 0, 0]
    background = torch.tensor(bg_color, dtype=torch.float32, device="cuda")

    iter_start = torch.cuda.Event(enable_timing = True)
    iter_end = torch.cuda.Event(enable_timing = True)

    viewpoint_stack = None
    ema_loss_for_log = 0.0
    progress_bar = tqdm(range(first_iter, opt.iterations), desc="Training progress")
    first_iter += 1
    for iteration in range(first_iter, opt.iterations + 1):
        if args.network_gui:
            if network_gui.conn == None:
                network_gui.try_connect()
            while network_gui.conn != None:
                try:
                    net_image_bytes = None
                    custom_cam, do_training, pipe.convert_SHs_python, pipe.compute_cov3D_python, keep_alive, scaling_modifer = network_gui.receive()
                    if custom_cam != None:
                        net_image = render(custom_cam, gaussians, pipe, background, scaling_modifer)["render"]
                        net_image_bytes = memoryview((torch.clamp(net_image, min=0, max=1.0) * 255).byte().permute(1, 2, 0).contiguous().cpu().numpy())
                    network_gui.send(net_image_bytes, dataset.source_path)
                    if do_training and ((iteration < int(opt.iterations)) or not keep_alive):
                        break
                except Exception as e:
                    network_gui.conn = None

        iter_start.record()

        # 更新 xyz 的学习率
        gaussians.update_learning_rate(iteration)

        # Every 1000 its we increase the levels of SH up to a maximum degree
        # 每迭代 1000 轮次将球谐的阶数 +1，直到达到最大阶数
        # if iteration % 1000 == 0:
        #     gaussians.oneupSHdegree()

        # Pick a random Camera
        # 随机选择一个图片和对应的视角参数（内外参）
        if not viewpoint_stack:
            viewpoint_stack = scene.getTrainCameras().copy()
        idx = randint(0, len(viewpoint_stack)-1)
        viewpoint_cam = viewpoint_stack.pop(idx)

        # Render
        if (iteration - 1) == debug_from:
            pipe.debug = True

        # 根据 3D Gaussian 渲染该相机视角下的图像
        render_pkg = render(viewpoint_cam, gaussians, pipe, background, cur_iters=iteration)
        # 渲染得到的图片
        image = render_pkg["render"]
        statics_image = render_pkg['render_stastic']
        occlus_image = render_pkg['render_occlu']
        view_opacity = render_pkg['view_opacity']
        # 所有 xyz 的梯度
        viewspace_point_tensor = render_pkg["viewspace_points"]
        # 有效 3D Gaussian 的选择矩阵：视锥内 and radii>0
        visibility_filter = render_pkg["visibility_filter"] 
        # 二维投影后的椭圆半径
        radii = render_pkg["radii"]

        # Loss
        gt_image = viewpoint_cam.original_image.cuda()
        
        # 在渲染图像和 GT 图像之间计算 loss                
        Ll1 = l1_loss(image, gt_image)
        loss = (1.0 - opt.lambda_dssim) * Ll1 + opt.lambda_dssim * (1.0 - ssim(image, gt_image))
        
        loss.backward()

        iter_end.record()
        
        look_up = 'look_up'
        os.makedirs(look_up, exist_ok=True)
        if iteration % 100 == 0:
            torchvision.utils.save_image(torch.cat([statics_image, occlus_image, image, gt_image], -1), os.path.join(look_up, f'{iteration:05d}-{idx}.png'))

        with torch.no_grad():
            # Progress bar
            ema_loss_for_log = 0.4 * loss.item() + 0.6 * ema_loss_for_log
            if iteration % 10 == 0:
                progress_bar.set_postfix({"Loss": f"{ema_loss_for_log:.{4}f}", "PSNR": f"{psnr(image, gt_image).mean().double():.{4}f}", "PC number": gaussians.get_xyz.shape[0]})
                progress_bar.update(10)
            if iteration == opt.iterations:
                progress_bar.close()

            # Log and save
            # training_report(tb_writer, iteration, Ll1, loss, l1_loss, iter_start.elapsed_time(iter_end), testing_iterations, scene, render, (pipe, background))
            if (iteration in saving_iterations):
                print("\n[ITER {}] Saving Gaussians".format(iteration))
                scene.save(iteration)
                        
            # Densification
            # 对 3D Gaussian 进行稠密化
            if iteration < opt.densify_until_iter:
                # Keep track of max radii in image-space for pruning
                # 将投影得到的椭圆半径更新到 max_radii2D 中进行记录   
                gaussians.max_radii2D[visibility_filter] = torch.max(gaussians.max_radii2D[visibility_filter], radii[visibility_filter])
                # 记录 xyz 的梯度 xyz_gradient_accum 的变化，用以进行稠密化
                gaussians.add_densification_stats(viewspace_point_tensor, view_opacity, visibility_filter)

                # 从某迭代轮次开始，每隔一定轮次进行 3D Gaussian 稠密化
                if iteration > opt.densify_from_iter and iteration % opt.densification_interval == 0:
                    size_threshold = 20 if iteration > opt.opacity_reset_interval else None
                    # 参数：xyz 梯度阈值、不透明度阈值、椭球体尺度阈值、投影椭圆的最大半径阈值
                    gaussians.densify_and_prune(opt.densify_grad_threshold, 0.005, scene.cameras_extent, size_threshold)
                
                # 每隔一定迭代轮次 or （背景为白色 and 开始稠密化时）
                # if iteration % opt.opacity_reset_interval == 0 or (dataset.white_background and iteration == opt.densify_from_iter):
                #     gaussians.reset_opacity()

            # Optimizer step
            if iteration < opt.iterations:
                gaussians.optimizer.step()
                gaussians.optimizer.zero_grad(set_to_none = True)

            if (iteration in checkpoint_iterations):
                print("\n[ITER {}] Saving Checkpoint".format(iteration))
                torch.save((gaussians.capture(), iteration), scene.model_path + "/chkpnt" + str(iteration) + ".pth")

def prepare_output_and_logger(args):
    if not args.model_path:
        args.model_path = os.path.join("./output/", args.source_path.split(os.sep)[-1])

    # Set up output folder
    print("Output folder: {}".format(args.model_path))
    os.makedirs(args.model_path, exist_ok = True)
    with open(os.path.join(args.model_path, "cfg_args"), 'w') as cfg_log_f:
        cfg_log_f.write(str(Namespace(**vars(args))))

    # Create Tensorboard writer
    tb_writer = None
    if TENSORBOARD_FOUND:
        tb_writer = SummaryWriter(args.model_path)
    else:
        print("Tensorboard not available: not logging progress")
    return tb_writer

def training_report(tb_writer:SummaryWriter, iteration, Ll1, loss, l1_loss, elapsed, testing_iterations, scene : Scene, renderFunc, renderArgs):
    if tb_writer:
        tb_writer.add_scalar('train_loss_patches/l1_loss', Ll1.item(), iteration)
        tb_writer.add_scalar('train_loss_patches/total_loss', loss.item(), iteration)
        tb_writer.add_scalar('iter_time', elapsed, iteration)

    # Report test and samples of training set
    if iteration in testing_iterations:
        scene.gaussians.eval()
        torch.cuda.empty_cache()
        validation_configs = (
            {'name': 'test', 'cameras' : scene.getTestCameras()}, 
            {'name': 'train', 'cameras' : [scene.getTrainCameras()[idx % len(scene.getTrainCameras())] for idx in range(5, 30, 5)]},
        )

        for config in validation_configs:
            if config['cameras'] and len(config['cameras']) > 0:
                l1_test = 0.0
                psnr_test = 0.0
                for idx, viewpoint in enumerate(config['cameras']):
                    render_pkg = renderFunc(viewpoint, scene.gaussians, *renderArgs)
                    image = torch.clamp(render_pkg["render"], 0.0, 1.0)
                    gt_image = torch.clamp(viewpoint.original_image.to("cuda"), 0.0, 1.0)
                    if tb_writer and (idx < 5):
                        tb_writer.add_images(config['name'] + "_view_{}/render".format(viewpoint.image_name), image, global_step=iteration, dataformats='CHW')
                        if iteration == testing_iterations[0]:
                            tb_writer.add_images(config['name'] + "_view_{}/ground_truth".format(viewpoint.image_name), gt_image, global_step=iteration, dataformats='CHW')
                    l1_test += l1_loss(image, gt_image).mean().double()
                    psnr_test += psnr(image, gt_image).mean().double()
                psnr_test /= len(config['cameras'])
                l1_test /= len(config['cameras'])          
                print("\n[ITER {}] Evaluating {}: L1 {} PSNR {}".format(iteration, config['name'], l1_test, psnr_test))
                if tb_writer:
                    tb_writer.add_scalar(config['name'] + '/loss_viewpoint - l1_loss', l1_test, iteration)
                    tb_writer.add_scalar(config['name'] + '/loss_viewpoint - psnr', psnr_test, iteration)

        if tb_writer:
            tb_writer.add_histogram("scene/opacity_histogram", scene.gaussians.get_opacity, iteration)
            tb_writer.add_scalar('total_points', scene.gaussians.get_xyz.shape[0], iteration)
        torch.cuda.empty_cache()
        scene.gaussians.train()

if __name__ == "__main__":
    # Set up command line argument parser
    parser = ArgumentParser(description="Training script parameters")
    lp = ModelParams(parser)
    op = OptimizationParams(parser)
    pp = PipelineParams(parser)
    parser.add_argument('--network_gui', action='store_true', help='enable network_gui for training monitor')
    parser.add_argument('--ip', type=str, default="127.0.0.1")
    parser.add_argument('--port', type=int, default=6009)
    parser.add_argument('--debug_from', type=int, default=-1)
    parser.add_argument('--detect_anomaly', action='store_true', default=False)
    parser.add_argument("--test_iterations", nargs="+", type=int, default=[7_000, 30_000])
    parser.add_argument("--save_iterations", nargs="+", type=int, default=[7_000, 30_000])
    parser.add_argument("--quiet", action="store_true")
    parser.add_argument("--checkpoint_iterations", nargs="+", type=int, default=[])
    parser.add_argument("--start_checkpoint", type=str, default = None)
    args = parser.parse_args(sys.argv[1:])
    args.save_iterations.append(args.iterations)
    
    print("Optimizing " + args.model_path)

    # Initialize system state (RNG)
    safe_state(args.quiet)

    # Start GUI server, configure and run training
    if args.network_gui:
        network_gui.init(args.ip, args.port)
    torch.autograd.set_detect_anomaly(args.detect_anomaly)
    training(lp.extract(args), 
             op.extract(args), 
             pp.extract(args), 
             args.test_iterations, 
             args.save_iterations, 
             args.checkpoint_iterations, 
             args.start_checkpoint, 
             args.debug_from)

    # All done
    print("\nTraining complete.")
